%\end{algorithm}
\end{figure}
+\subsection{Simulation of two-stage methods using SimGrid framework}
+\label{sec:04.02}
+One of our objectives when simulating the application in SIMGRID is, as in real life, to get accurate results (solutions of the problem) but also ensure the test reproducibility under the same conditions.According our experience, very few modifications are required to adapt a MPI program to run in SIMGRID simulator using SMPI (Simulator MPI).The first modification is to include SMPI libraries and related header files (smpi.h). The second and important modification is to eliminate all global variables in moving them to local subroutine or using a Simgrid selector called "runtime automatic switching" (smpi/privatize\_global\_variables). Indeed, global variables can generate side effects on runtime between the threads running in the same process, generated by the Simgrid to simulate the grid environment.The last modification on the MPI program pointed out for some cases, the review of the sequence of the MPI\_Isend, MPI\_Irecv and MPI\_Waitall instructions which might cause an infinite loop.
+\paragraph{SIMGRID Simulator parameters}
+\begin{itemize}
+ \item HOSTFILE: Hosts description file.
+ \item PLATFORM: File describing the platform architecture : clusters (CPU power,
+\dots{}), intra cluster network description, inter cluster network (bandwidth bw,
+lat latency, \dots{}).
+ \item ARCHI : Grid computational description (Number of clusters, Number of
+nodes/processors for each cluster).
+\end{itemize}
+In addition, the following arguments are given to the programs at runtime:
+\begin{itemize}
+ \item Maximum number of inner and outer iterations;
+ \item Inner and outer precisions;
+ \item Matrix size (NX, NY and NZ);
+ \item Matrix diagonal value = 6.0;
+ \item Execution Mode: synchronous or asynchronous.
+\end{itemize}
-
-\subsection{Simulation of two-stage methods using SimGrid framework}
+At last, note that the two solver algorithms have been executed with the Simgrid selector --cfg=smpi/running\_power which determine the computational power (here 19GFlops) of the simulator host machine.
%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Experimental, Results and Comments}
-\textbf{V.1. Setup study and Methodology}
+\subsection{Setup study and Methodology}
To conduct our study, we have put in place the following methodology
which can be reused with any grid-enabled applications.
input matrix. Note that some parameters should be invariant to allow the
comparison like some program input arguments.
-\textbf{Step 6} : Collect and analyze the output results.
+{Step 6} : Collect and analyze the output results.
-\textbf{ V.2. Factors impacting distributed applications performance in
+\subsection{Factors impacting distributed applications performance in
a grid environment}
From our previous experience on running distributed application in a
between distant clusters might be a bottleneck for the global
performance of the application.
-\textbf{V.3 Comparing GMRES and Multisplitting algorithms in
+\subsection{Comparing GMRES and Multisplitting algorithms in
synchronous mode}
In the scope of this paper, our first objective is to demonstrate the
Table 1 : Clusters x Nodes with NX=150 or NX=170
-\RCE{J'ai voulu mettre les tableaux des données mais je pense que c'est inutile et ça va surcharger}
+%\RCE{J'ai voulu mettre les tableaux des données mais je pense que c'est inutile et ça va surcharger}
The results in figure 1 show the non-variation of the number of
after adding more powerful CPU. Note that the execution time axis in the
figure is in logarithmic scale.
- \textbf{V.4 Comparing GMRES in native synchronous mode and
+\subsection{Comparing GMRES in native synchronous mode and
Multisplitting algorithms in asynchronous mode}
The previous paragraphs put in evidence the interests to simulate the
